Uberization of Knowledge Work

Meena is the solo growth lead at a Series-A fintech that turns round-ups into micro-investments, half quant, half hustler, perpetually wired. While she sleeps, three custom agents she cobbled together in LangChain + Zapier run the night shift:

• One pulls yesterday's Stripe events, clusters high-LTV cohorts, and flags a spike in users auto-investing after reading the blog's "compound-interest meme" post.

• Another scrapes r/personalfinance and TikTok's #MoneyTok, surfaces fresh pain points ("inflation hedge anxiety"), and drafts SEO briefs plus an email-subject matrix around them.

• The third spider-crawls competitor sitemaps, scores keyword gaps, and schedules six long-form posts into the CMS, complete with FAQ-rich-snippet markup.

The only human collaborator is Aditi in Bengaluru, a freelance media buyer with a killer eye. She spins the top Reddit insight into a thumb-stopping carousel: Midjourney for graphics, Figma for polish, Meta Ads Manager for laser targeting. By the time Meena pours her first coffee, the dashboard are already buzzing, and her "role" is reduced to steering—approve the creative, tweak bid caps, recycle the freed budget into the next SEO sprint. Everything else flows through the mesh of agents she treats like temp interns that never ask for equity.

That scene is no longer speculative. It rests on infrastructure anyone can assemble with off-the-shelf tools. The capacity to delegate cognition itself, data gathering, pattern finding, first-draft synthesis, to a mesh of APIs is now a practical skill, not futurist fan-fic.

Cognition itself is becoming a distributed service.

Tasks unbound from headcount and cognition unbound from biology are converging at warp speed. The question that now stalks every founder, operator, and strategist is disarmingly basic: what role do we humans play at the core of knowledge work?

What that means for the ownership of thinking, craft, and ultimately livelihood.

Gigification of Cognition

Step out of your apartment in any major city and you can feel the invisible choreography that gets you a car in ninety seconds. Tap, and somewhere a driver's phone lights up; seconds later, a real-world object is rerouted toward you by software instincts. That same choreography is quietly leaking into white-collar life. Except the "vehicles" now gliding toward tasks are not Priuses—they're fragments of cognition marshalled by APIs, LLMs, and personal knowledge graphs. What was once a desk-bound act of concentration is turning into a dynamic dispatch layer where judgment moves through digital traffic like packets on a network.

This is not futurism, but the reality of teams building GPT-driven research loops, marketers who start their day with a Slack digest written by a cluster of background agents, and founders who treat their note-taking graph as an executable environment.

When work is Uberizing; are we in a position to even understand what that does to power, ownership, and the texture of creativity.

A decade of SaaS fragmentation has chipped full-time roles into micro-specialties. Strategy decks, CRO audits, even growth experiments now circulate like ride requests—priced per task, matched in seconds, cleared on delivery. The friction that once kept work inside payroll walls has evaporated: Slack channels replace corridors, Stripe replaces payroll, time-zones blur into a single relentless sprint. For marketers, this means every slice of the funnel can be auctioned off to the best responder on Upwork, Contra, or Fiber—and often is. The corporate org chart is quietly dissolving into a loose federation of project IDs.

Liquid Labour Market & LLMs as Synthetic Colleagues

We are one product cycle away from seeing these trends fuse.

When a company can "hire" a pod of pretrained agents, spin up H100s, load role-specific wrappers, and pay by the minute, LLMs stop being software and start behaving like remote freelancers. The recruiting funnel collapses into a Terraform script.

APIs began as plumbing between databases. Today they are past that ontological threshold interpreting context, brokering judgment, allocating mental cycles with the same callous efficiency Uber applies to idle sedans. Into that liquid cognitive labor market step language models, but fully programmable teammates. An LLM can scrape, parse, hypothesise, and draft faster than onboarding a junior hire. The model rewrites headlines in the tone of last quarter's highest-converting copy, segments audiences on the fly, and surfaces anomalies in conversion paths before analytics tools trigger their alerts. Crucially, it does so on demand, metered by the token, cheaper than the snack budget for an intern.

In short, the API is now a semi-autonomous broker of meaning.

Consider running an outbound campaign. A webhook catches fresh CRM records, an embedding service clusters them by intent, a generative model drafts personalized copy, and a scheduling API staggers sends to dodge spam filters. Nobody "opens" a dashboard until a high-temperature anomaly pings their phone. The orchestration layer has quietly eaten the middle of the knowledge-work stack: research, triage, initial decision, and even micro-iterations.

Yet a liquid labour market re-codes value. When cognition fragments into callable micro-services, the half-finished sketch on your whiteboard is less billable than the neatly packaged JSON your agent can forward. The more legible the thought, the more replaceable the thinker. Uber's surge algorithm never cared about a driver's local banter, only the GPS dot; platformised cognition exhibits a similar indifference to idiosyncrasy.

Your economic leverage is therefore no longer capped by the wattage of a single, biological brain but by how well you design, own, and steer an external brain—the ensemble of tools, data, and AI agents that execute an ever-growing slice of cognition on your behalf.

In the wake of this new era, human cognition is invited to zoom out: to audit routing rules, refine prompts, and decide where the loop should fork next. The tactical "first pass" is already mediated, and it is happening at machine speed.

What Humans Do Next

As low-level cognition externalises into commodity APIs, the residue left to people shifts upward: defining the brief, architecting the system, adjudicating edge-cases, metabolising ethics. Some will celebrate the elevation; others will see it as a hollowing-out. Either way, the next five years promise a violent repricing of expertise. Gig-style logistics prepared the runway, LLMs are taxiing for take-off. That's why understanding what it takes to navigate both a career and an organization in this new reality has become crucial.

The External Brain as Vehicle

If APIs are the dispatchers, your external brain is the fleet. Once a personal knowledge base was little more than a synced folder labeled "Notes." Now it is a polyglot organism: markdown files, vector indices, browser-based canvases, and real-time streams of metabolic data about what you read, say, and decide. Tools like Metaflow or Obsidian's dataview plug-in let a paragraph written at 2 a.m. reappear days later, context-aware, as a suggestion inside a project brief drafted by an LLM.

The cognitive vehicle is therefore neither wholly human nor wholly machine. It is a hybrid chassis—your memories, assumptions, and tacit know-how lashed to statistical engines that remix them at inference time. When you instruct a language model to "draft an investor update in my style," you mobilize a memory co-authored by you and a dozen models. Cognition unbundles from the skull and recomposes as a hybrid mesh: part human intuition, part statistical ventriloquism. You are effectively renting your own intellectual exhaust: a corpus you authored but could never re-index that quickly without help.

This co-agency is intoxicating. The thinker becomes a systems architect, deciding which lanes of the graph get live context, which stay frozen for audit, and which hallucination merits human review. In the best cases, judgment scales: a single strategist can test twenty market narratives overnight because half the pipeline is handled by silent cognitive chauffeurs.

The danger is a gradual slide from driver to passenger, lulled by the illusion of hands-free momentum.

Commodification, Dependency, and Power

Yet the same protocol that amplifies your reach can also affect your craft. Uber's genius was to repackage a local, relationship-laden service (the neighborhood taxi) into a fungible commodity. Surge pricing proved that attention itself could be spot-priced. The emerging cognitive stack risks a similar calculus. If a paragraph of competitive analysis can be atomized into "research this domain ➜ synthesize key risks ➜ draft summary," then the act is legible to automation—and legibility is the precondition for commodification.

Platform logic rewards tasks that can be priced, scheduled, and benchmarked. The more you pluginize thinking, the more your uniqueness must live elsewhere: in the prompts no one sees, the private heuristics that resist full articulation, the messy hunch you jot on paper before feeding the sanitized version to your graph. Otherwise, creative work risks becoming the linguistic equivalent of a rideshare ride: efficient, auditable, replaceable.

Another control issue is data and dependency. In an Uberized model, do you own your tools and data, or are you merely renting them? Uber drivers famously don't own the customer relationship or the pricing model; the platform does. Likewise, if your external brain lives across proprietary clouds—closed embeddings here, usage-metered prompts there—your cognitive vehicle can be repossessed overnight by a tweak in terms of service or a hike in token costs.

And the exhaust matters. Platforms may capture every query, every correction, every edge-case prompt—training against your private heuristics the way social networks harvest engagement. Governance migrates upstream into model weights and billing dashboards, places most operators never inspect. Control over the orchestration layer is the new form of power. Who writes the script your agents follow? Is it you, your employer, a vendor, or an emergent blend no one quite oversees?

Control complicates matters further. Routing rules look objective, but they encode values: speed over context, breadth over depth, pattern over exception. When an API silently rejects a low-confidence lead, whose epistemology is at play? The vendor who tuned the threshold? The founder who chose throughput over nuance? Governance migrates upstream into pre-trained weights and default configs—areas most operators neverinspect. Autonomy is ceded one silent default at a time. Control over the orchestration layer is the new form of power. Who writes the script your agents follow? Is it you, your employer, a vendor, or an emergent blend no one quite oversees?

What Remains Distinctively Human Is the Meta-Work

If tactical cognition is liquefying, the scarce skill is meta-work: designing feedback loops, deciding which questions even deserve agents, curating the boundary where ambiguity must remain human. Strategy mutates from direct problem-solving to problem-framing—choosing which constraints matter, which signals count as truth, which externalities you refuse to ignore because the cost of ignoring them is borne by someone else.

This meta-layer is not an optional aesthetic; it is survival. Without deliberate schema, your graph calcifies into a data swamp. Without friction points, your agents optimize for metrics that look like insight while eroding judgment. And without private entropy—sketchbooks, side rants, half-baked notions kept offline—you risk becoming fully legible and therefore fully priceable.

Survival is a baseline, not a thesis.

We are living through a hinge in intellectual history where the labour of thought can finally exceed the carrying capacity of a single cortex. That realization feels both exciting and unsettling. I'm trying to make sense of it too. Declining that invitation is like feather-footing a supercar, revving potential without motion. The coming decade belongs to those who cultivate an exoneural skeleton—a lattice of prompts, schemas, and agentic pipelines that extend memory, inference, and creative recombination well beyond the skull.

This is less an act of outsourcing thinking over to machines; it's about giving our own thinking better scaffolding—much like language once let raw consciousness find shape.

Yet every scaffold comes with questions of control. If you abstain from building your own, you cede governance to whichever SaaS broker first captures your workflow. Engage with it, and you shift from knowledge worker to knowledge architect, curating a cognitive estate that compounds like capital. I feel both awe at what these systems can already do and unease at how quickly default settings can sand away originality.

Ontology, context, and governance are converging into one design surface; the line between "tool" and "co-author" now hinges on how deliberately we audit the handshake.

There's no tidy answer. A poorly governed network can commodify attention and hollow judgment, reducing cognition to a thin API call. A well-governed one can liberate attention, letting humans allocate scarce focus to framing the questions rather than brute-forcing the answers.

That's why hybrid work with AI isn't an edge case anymore; it is the new minimum viable agency.

In that light, tools like Metaflow aren't just software; they're the rivets in that living skeleton, places where we decide what stays human, what goes machine, and how to keep that handshake honest.

Armouring the External Cognitive Engine aka a living breathing Second Brain

A field-note on evolving an external cognition that stays light, alive, and entirely yours.

1. Work in Small, Reusable Steps

Instead of designing the perfect "external brain," I've begun asking one low-stakes question whenever I add anything: "Will this help me solve a real problem this week?" If the answer is yes, in it goes. If not, I wait. Each addition stays tiny and test-able, which keeps the system pliable when tomorrow's questions change.

Treat your external brain as an evolving schema, not a dump.

2. Retire the Architect, Hire the Gardener

Big-bang architectures promise permanence and deliver brittleness. Gardeners do the opposite: they prune, graft, and let the living system re-organise around real sunlight. Build for this week's question, not for some speculative future workflow. The hedge will reshape itself as new use-cases appear; premature fortresses merely fossilise yesterday's assumptions.

3. Stand on Platforms, Swap the Tools as Needed

Staying in the driver's seat demands intentional infrastructure.

Think of tools like shells for a hermit crab, useful, but temporary. You can (and should) trade up when a better fit appears. What you don't want is to scuttle around bare-shelled, sketching your grand vision on cocktail napkins while everyone else is shipping work.

Begin with a real platform, a backbone that already understands tagging, search, and basic automation and let it carry the weight of infrastructure. Why reinvent wheels that'd already been stress-tested for exactly this use-case?

A simple backbone I lean on:

4. Small things matter - Ontology First, Always

Simple tags, folders, and status flags beat clever automations every time, especially on sprawling projects or long essays where links matter more than volume. Before dropping anything into Notion (or whatever you use), add one quick label:

• Topic (ties it to a theme)

• Role (reference, raw source, reflection)

• Status (draft, working, done)

Those three cues give future-you—and any script you bolt on later—something solid to grip. The payoff shows up months down the road when you can summon a networked outline in minutes instead of spelunking through a junk drawer.

Structure invites selective automation; chaos invites blunt automation.

5. Insert Deliberate Friction

Design friction points.

Big, end-to-end workflows look heroic but break like glass. I've switched to sketching micro-loops: one tidy sequence that pulls data, nudges me for judgment, then outputs something useful. Breaking the flow into snap-off segments makes the pause easy; when one link misbehaves, I mend that single link instead of rebuilding the whole chain.

Insert checkpoints where the model must surface intermediate reasoning, letting you veto, redirect, or deepen the inquiry.

Bonus: being the human hinge between modules keeps me tuned to edge-cases the model still can't smell.

6. Guard the Meta-Layer—It’s the Real Asset

Cultivate private entropy.

The real asset class isn't the finished slide deck or polished article; it's the upstream circuitry that makes those artifacts possible: prompts you've tuned, workflow graphs you've pruned, swipe-files of inspirations that keep the creative engine lit. That meta-layer is the engine block of your exoneural rig, and it's IP you actually own. Catalogue, version, and protect them with the same rigor you'd devote to source code.

• Keep a vault for your prompts

• Catalog your workflows, templates, style guides—they're your cognitive blueprints

• Maintain a swipefile like personal directory to store inspirational shards (copies, artifacts, designs, images) with the context of why they clicked for you.

Organised well, this library compounds. Disorganised, it evaporates into the feed scroll. These are the high-margin intangibles in the cognitive value chain. They aren't decorative add-ons to your external brain; they are its drivetrain, the compounding engine that turns raw inputs into differentiated leverage and, ultimately, monetizable advantage.

Who Owns the Vehicle?

When ridesharing hit scale, the cultural debate fixated on labor classification: Was a driver an employee or a contractor? The deeper question is ontological: What happens when an algorithm allocates human effort the way a trading desk allocates capital? Knowledge work is entering that phase now. We may soon ask whether a "research sprint" executed by intertwined agents is labor, tooling, or a new category altogether.

Yes, the dispatch layer can free us from low-leverage drudgery, letting human judgment roam at strategic altitude. But the same layer can quietly meter our ingenuity, turning nuanced thought into billable micro-tasks traversing someone else's network topology.

If entire sectors channel cognition through a handful of proprietary orchestration layers, the macro risks resemble monopoly control of roads:

• Pricing power. Platform toggles a higher API tariff; your margins evaporate.

• Epistemic capture. The platform's ranking algorithm shapes which ideas circulate—subtly steering collective attention.

• Data asymmetry. Your workflows train their models; their models commoditize your niche.

The remedy is pluralism: open protocols for agent hand-offs, portable data schemas, and public oversight as robust as we demand for roads or radio spectrum. But pluralism only works if every node in the network assumes an augmented operator—someone whose "desk" is a living lattice of LLMs and workflow scaffolds where prompts, context memories, and decision policies are stored, version-controlled, and continuously recombined. Without that scaffold, your judgment ends up trapped inside someone else's black box, and the leverage that should compound to you compounds to the platform.

Extended cognition, then, is no longer a philosophical sidebar; it is the economic substrate of modern work. A single practitioner armed with a well-tuned external brain can match—or eclipse—the throughput of an entire pre-AI department. The downside is silent deskilling and creeping dependence on opaque systems.

Why the Next Five Years Matter

The repricing of expertise will not wait for thoughtful governance. Every quarter the marginal cost of competent analysis, drafting, and pattern-finding falls, while the premium migrates to those who can choreograph a fleet of agents as naturally as they once opened a spreadsheet. The gap between doing the work and orchestrating the work is widening—fast.

Return to Meena, Now Five Years On

Return to Meena, the solo growth lead whose overnight lattice of agents did everything but sip her coffee.

Her external brain—stitched together with tools like Metaflow—compressed eight human-hours into minutes, cut costs by orders of magnitude, and lapped a pre-AI department before dawn. Scale that leverage across millions of workflows and you get the real stakes of owning the cognitive vehicle:

• Design the engine, don't rent it. Whether you wire it together in Metaflow or with your own duct-taped stack, treat your external brain as critical infrastructure—version-controlled prompts, transparent data flows, auditable decision logs.

• Keep hands on the wheel. Review gates, sanity checks, "single-step" dry runs—habits that ensure you still recognise the road when autopilot drifts.

• Invest in open roads. Portable schemas and agent hand-offs aren't just nice-to-haves; they're escape lanes when platforms change the tolls.

Do this well and the math compounds in your favour: hours saved become cycles for deeper questions, dollars saved become fuel for bolder experiments, and every workflow you sculpt becomes a durable cognitive asset.

That is what it means to own the vehicle: not clinging to any one model or vendor, but shaping the chassis, your personalised, extensible engine for thinking and doing.

Meena still leads growth, but the job description is unrecognizable.

What consumes her calendar today?

• Framing the problems. She decides which signals matter before an agent hunts for them.

• Governing the system. She reviews audit logs, tests prompt drift, and swaps models when bias or cost creeps in.

• Cultivating new edges. She invents workflows her competitors have not dreamt of—then retires them the moment they commoditize.

The dashboards still light up at dawn, but Meena owns the circuitry behind them. That ownership is the new moat.

The Uberization of knowledge work need not shrink human agency. Done well, it does the opposite: routine tasks dissolve; curiosity scales; creativity moves upstream. The steering wheel is still within reach—grab it.

The next five years will belong to those who build deliberate external minds. Start sketching yours now; the meter is already running.